Section 128 · Chapter 16, Introspection: White-Box Testing Networks
Layer-by-Layer Attention Matrices
Attention matrices expose token-to-token routing patterns that can be compared across layers, prompts, and model versions.
attentionlayer by layer attention matrices
What to do
- Preserve the unaggregated heads because an average can hide a specialized head or disagreement among heads.
- Save a few representative layers for known-good and known-bad cases.
- Define runnable checks that exercise attention and layer by layer attention matrices.
Evidence to preserve
- Preserve the unaggregated heads because an average can hide a specialized head or disagreement among heads.
- Save a few representative layers for known-good and known-bad cases.
- Preserve the inputs, versions, configurations, raw outcomes, and results for attention, layer by layer attention matrices needed to reproduce work on Layer-by-Layer Attention Matrices.
- Report results for attention, layer by layer attention matrices by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Attention matrices should be treated as diagnostic artifacts. Pair them with ablation, activation patching, counterfactual prompts, and output evals before drawing causal conclusions.
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Cite this page
Jason Arbon. "Layer-by-Layer Attention Matrices." Testing AI Knowledge Edition, section 128.
https://jarbon.ai/testing-ai/knowledge/ch128-layer-by-layer-attention-matrices.html